A new framework based on features modeling and ensemble learning to predict query performance.

ABSTRACT: A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to pre

SUBMITTER: Zaghloul M 

PROVIDER: S-EPMC8523072 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature